System and method for quality control in healthcare settings to continuously monitor outcomes and undesirable outcomes such as infections, re-operations, excess mortality, and readmissions

ABSTRACT

A method for inferring a probability of a first inference. The inference is related to identification of a cause of an outcome in a healthcare setting. A fact, related to the query, is related to the healthcare setting. The fact further relates to a network of interactions associated with the outcome. Each datum of the database conforms to the dimensions of the database. Each datum of the plurality of data has associated metadata and an associated key. The associated metadata includes data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum. The query establishes a frame of reference for the search. The database returns a probability of the correctness of the first inference based on the query and on the data.

RELATED APPLICATION

This application is a continuation-in-part of patent application U.S.Ser. No. 11/678,959, filed Feb. 26, 2007, now U.S. Pat. No. 7,752,154titled “System and Method for Deriving a Hierarchical Event BasedDatabase Optimized for Analysis of Criminal and Security Information”.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to an improved data processingsystem and in particular to a method and apparatus for searching data.More particularly, the present invention relates to a computerimplemented method, apparatus, and a computer usable program product foran event-based database for analyzing security information to discoverpast, present, or future potentially criminal activities.

2. Description of the Related Art

Combating terrorism and crime effectively often depends on accurateinformation. For example, if the location or exact identity of aterrorist or criminal is not known, then apprehending the terrorist orcriminal is difficult, if not impossible. Thus, methods and devices forbetter acquiring and processing information are always desired in theareas of law enforcement and the military.

Finding anomalous criminal or terrorist activities in a sea ofinformation is extraordinarily difficult under the best ofcircumstances. Pertinent information is often buried in vast quantitiesof divergent data. Divergent data is sets of data having differenttypes, sizes, compatibilities, and other differences. The data is oftenof widely different types scattered across various physical systemsbelonging to different organizations or individuals. Many of the datatypes, such as picture files, video files, and audio files, are notnormally susceptible to normal query techniques. Relevant information isoften spread through different points in time. The data is stored oftenat different levels of granularity; that is, some data has a great dealof associated information while other data has only a little associatedinformation.

Additionally, the data often reflect parts of larger patterns. A firstset of data, by itself, is of little value, but together with other datacombinations of the first set of data and other data would show apattern of criminal or terrorist activity. Similarly, patterns or eventsare often discernable only by piecing together data from multipleindividuals or cohorts spread throughout the data. Cohorts are groups ofobjects or people that share common characteristics or are otherwisepart of a group.

To make matters more difficult, not all data is accessible to theindividuals to whom the data would matter most. For example, a citydetective might not have access to databases of the Federal Bureau ofInvestigation or the Central Intelligence Agency. Thus, the citydetective might not have access to information critical to solving acrime or disrupting a terrorist plot. Similarly, lack of a longitudinalview of criminal or security related events hampers the ability of lawenforcement personnel, military personnel, or intelligence analysts frommaking important inferences that would solve crimes or prevent nefariousactivities. Furthermore, much of the available data is subjective orambiguous.

Databases, data processing systems, and information processing systemshave been proposed to attempt to address this problem. However, allknown information processing systems suffer from critical flaws, such asin the lack of an ability to deal with data at different levels ofgranularity, or the lack of the ability to compare divergent data andassign multiple levels of granularity and probability to inferences thatcan be made from the divergent data.

SUMMARY OF THE INVENTION

Illustrative embodiments provide a computer implemented method,apparatus, and computer usable program code for inferring a probabilityof a first inference related to identification of a cause of an outcomein a healthcare setting. The method includes receiving at a databaseregarding a fact related to the healthcare setting. The fact furtherrelates to a network of interactions associated with the outcome. Thefirst inference is absent from the database. The database includes aplurality of divergent data. The plurality of divergent data includes aplurality of cohort data. Each datum of the database is conformed to thedimensions of the database. Each datum of the plurality of data hasassociated metadata and an associated key. The associated metadataincludes data regarding cohorts associated with the corresponding datum,data regarding hierarchies associated with the corresponding datum, dataregarding a corresponding source of the datum, and data regardingprobabilities associated with integrity, reliability, and importance ofeach associated datum. The method further includes establishing the factas a frame of reference for the query and applying a first set of rulesto the query. The set of rules are determined for the query according toa second set of rules. The first set of rules determine how theplurality of data are to be compared to the fact. The first set of rulesdetermine a search space. The method also includes executing the queryto create the probability of the first inference. The probability of thefirst inference is determined from comparing the plurality of dataaccording to the first set of rules. The method also includes storingthe probability of the first inference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 is pictorial representation of a data processing system in whichthe aspects of the present invention may be implemented;

FIG. 2 is a block diagram of a data processing system in which aspectsof the present invention may be implemented;

FIG. 3 is a block diagram illustrating a prior art method of analyzingdata in an attempt to identify past, present, or future criminalactivity;

FIG. 4 is a block diagram illustrating a central database used foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 5 is a block diagram of a data processing network used inconjunction with a central database for identifying past, present, orfuture criminal activity, in accordance with an illustrative embodiment;

FIG. 6 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 7 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 8 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 9 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 10 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 11 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 12 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 13 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 14 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 15 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 16 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 17 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment;

FIG. 18 is a block diagram of illustrating components and operatingcharacteristics of a central database for identifying past, present, orfuture criminal activity, in accordance with an illustrative embodiment;

FIG. 19 is a block diagram illustrating subsystems for selection andprocessing of data using a central database for identifying past,present, or future criminal activity, in accordance with an illustrativeembodiment;

FIGS. 20A and 20B are an exemplary structure of a database that can beused for a central database, in accordance with an illustrativeembodiment;

FIG. 21 is a flowchart illustrating establishment of a database adaptedto establish a probability of an inference based on data contained inthe database, in accordance with an illustrative embodiment;

FIG. 22 is a flowchart illustrating execution of a query in a databaseto establish a probability of an inference based on data contained inthe database, in accordance with an illustrative embodiment; and

FIGS. 23A and 23B are a flowchart illustrating execution of a query in adatabase to establish a probability of an inference based on datacontained in the database, in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

With reference now to the figures, FIG. 1 depicts a pictorialrepresentation of a network of data processing systems in whichillustrative embodiments may be implemented. Network data processingsystem 100 is a network of computers in which embodiments may beimplemented. Network data processing system 100 contains network 102,which is the medium used to provide communications links between variousdevices and computers connected together within network data processingsystem 100. Network 102 may include connections, such as wire, wirelesscommunication links, or fiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. These clients 110, 112, and 114 may be, forexample, personal computers or network computers. In the depictedexample, server 104 provides data, such as boot files, operating systemimages, and applications to clients 110, 112, and 114. Clients 110, 112,and 114 are clients to server 104 in this example. Network dataprocessing system 100 may include additional servers, clients, and otherdevices not shown.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation fordifferent embodiments.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer usable code orinstructions implementing the processes may be located for theillustrative embodiments.

In the depicted example, data processing system 200 employs a hubarchitecture including a north bridge and memory controller hub (MCH)202 and a south bridge and input/output (I/O) controller hub (ICH) 204.Processor 206, main memory 208, and graphics processor 210 are coupledto north bridge and memory controller hub 202. Graphics processor 210may be coupled to the MCH through an accelerated graphics port (AGP),for example.

In the depicted example, local area network (LAN) adapter 212 is coupledto south bridge and I/O controller hub 204 and audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) ports and other communications ports 232, andPCI/PCIe devices 234 are coupled to south bridge and I/O controller hub204 through bus 238, and hard disk drive (HDD) 226 and CD-ROM drive 230are coupled to south bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM drive230 may use, for example, an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. A super I/O(SIO) device 236 may be coupled to south bridge and I/O controller hub204.

An operating system runs on processor 206 and coordinates and providescontrol of various components within data processing system 200 in FIG.2. The operating system may be a commercially available operating systemsuch as Microsoft® Windows® XP (Microsoft and Windows are trademarks ofMicrosoft Corporation in the United States, other countries, or both).An object oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java programs or applicationsexecuting on data processing system 200 (Java and all Java-basedtrademarks are trademarks of Sun Microsystems, Inc. in the UnitedStates, other countries, or both).

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as hard disk drive 226, and may be loaded into main memory 208 forexecution by processor 206. The processes of the illustrativeembodiments may be performed by processor 206 using computer implementedinstructions, which may be located in a memory such as, for example,main memory 208, read only memory 224, or in one or more peripheraldevices.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. Also, the processes of the illustrative embodiments may be appliedto a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may be comprised of oneor more buses, such as a system bus, an I/O bus and a PCI bus. Of coursethe bus system may be implemented using any type of communicationsfabric or architecture that provides for a transfer of data betweendifferent components or devices attached to the fabric or architecture.A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache such as found in north bridgeand memory controller hub 202. A processing unit may include one or moreprocessors or CPUs. The depicted examples in FIGS. 1-2 andabove-described examples are not meant to imply architecturallimitations. For example, data processing system 200 also may be atablet computer, laptop computer, or telephone device in addition totaking the form of a PDA.

Illustrative embodiments provide a computer implemented method,apparatus, and computer usable program code for creating and using acentralized database for managing information. The centralized databasecan be used to derive probabilities of inferences based on comparison ofdata within the centralized database according to a set of search rules.The search rules are, themselves, determined by a set of determinationrules. Thus, the system prevents the entirety of the data in thedatabase from being compared in every possible combination in order thatlimited computing resources can execute desired queries. The system isparticularly useful in the context of criminal investigations orintelligence services where vast quantities of data are to be sifted.

Many of the systems, items, or persons shown throughout FIG. 3 throughFIG. 19 are similar. Thus, similar reference numerals in these figuresrefer to similar items.

FIG. 3 is a block diagram illustrating a prior art method of analyzingdata in an attempt to identify past, present, or future criminalactivity. The prior art method shown can be implemented by one or moreusers using one or more data processing systems, such as server 104,server 106, client 110, client 112, and client 114 in FIG. 1, and dataprocessing system 200 shown in FIG. 2. These data processing systems cancommunicate over a network, such as network 102 shown in FIG. 1.

As shown in FIG. 3, analyst 300 and analyst 302 receive information froma variety of sources of information and attempt to derive inferencesfrom the variety of sources of information. Sources of information canbe any source of information, such as video camera footage, newsaccounts, reports from field operatives, police reports, police radiotransmissions, voice recordings, or nearly any kind of informationsource. To show the complexity of the problem, many data sources areshown, such as data source 304, data source 306, data source 308, datasource 310, data source 312, data source 314, data source 316, and datasource 318. Analyst 300 and analyst 302 also may input data back intosome of the data sources.

The arrows show the direction of information from the sources and theanalysts. An arrow pointing away from an analyst means that the analystis able to input data into a source, but not to receive data from thesource. An arrow pointing toward an analyst means that the analyst isable to receive data from a source, but not to input data into thesource. An arrow pointing both directions indicates that the analyst isable to both input data into the source and receive data from thesource.

Thus, for example, analyst 302 can only input data into source 304, asshown by arrow 320. Both analyst 300 and analyst 302 can input data toand receive data from source 306, as shown by arrows 322 and 324.Analyst 300 can both input data into and receive data from source 308,as shown by arrows 326; however, analyst 302 has no access whatsoever tosource 308. Similarly, analyst 300 can receive data from and input datato source 310, as shown by arrows 328, while analyst 302 cannot accesssource 310 at all. Analyst 302 can only input data into source 312, asshown by arrow 330.

Analyst 300 can input data into source 314; however, only analyst 302can receive data from source 314, as shown by arrows 332 and 334.Analyst 302 can receive data from 316, but cannot input data to source316, as shown by arrows 336. Analyst 302 can input data to source 318,but cannot receive data from source 318, as shown by arrow 338. Analyst300 cannot access source 316, source 318, or source 304.

Analyst 300 and analyst 302 can send and receive data from each other.However, because analyst 300 and analyst 302 do not have the same levelof access to information, both analysts are subject to blind spots ininformation and are thus unable to make certain inferences that could becritical to solving a case or even stopping a terrorist attack withweapons of mass destruction.

For example, analyst 300 receives data from source 308 that indicatesthat Suspect purchased one thousand pounds of high nitrate fertilizerjust prior to the planting season in the state of X. Analyst 300 doesnot consider the purchase to be important because large quantities ofhigh nitrate fertilizer are often purchased at the given time of year.

On the other hand, analyst 302 receives data from source 316 thatindicates that Suspect has moved to the state of X. Analyst 302 receivesfurther information from source 314 that Suspect is a member of acriminal organization infamous for bombing government buildings and thatSuspect has expert bomb-making skills from military service. Analyst 302considers the information somewhat important. However, because analyst302 lacks any other evidence or information, analyst 302 simply inputsinto source 318 the fact that Suspect in the state of X.

Combined, the facts that Suspect purchased 1000 pounds of high nitratefertilizer, that Suspect moved to the state of X, that Suspect is amember of a criminal organization infamous for bombing governmentbuildings, and that Suspect is an expert bomb maker creates an inferencethat a high degree of probability exists that Suspect intends to engagein criminal or terrorist activities.

However, analyst 300 cannot make this inference because analyst 300 onlyknows that Suspect purchased high nitrate fertilizer at a time of yearwhen such purchases are normally made. On the other hand, analyst 302cannot make this inference because analyst 302 does not know thatSuspect has purchased a large quantity of high nitrate fertilizer.

Still more problematically, the fact that analyst 300 and analyst 302can communicate with each other may be of no assistance. Unless byhappenstance analyst 300 and analyst 302 discuss these facts together,neither analyst will make the inference that Suspect poses a clear andpresent danger. However, analyst 300 and analyst 302 are unlikely todiscuss the matter because analyst 300 has no reason to believe that thehigh nitrate fertilizer purchase is abnormal and analyst 302 has noreason to believe that Suspect may be currently engaged in criminalactivity.

As a result, Suspect may be able to execute a bomb attack on agovernment building without prior interference. In retrospect, after anattack, analyst 300 and analyst 302 might be able to infer that togetherthey had the necessary information. However, without the hindsightknowledge of the fact of the attack they probably would be unable tomake the inference. While making the inference in hindsight might bevaluable to finding and prosecuting Suspect after the attack, lawenforcement personnel would prefer to thwart the attack in the firstplace.

Note that the inference that Suspect is engaging in a plot to build abomb and then use the bomb in a terrorist activity is not one hundredpercent reliable. For all analyst 300 and analyst 302 know, Suspect mayhave left the criminal organization and mended his ways. To make aliving, he became a farmer and has need for the high nitrate fertilizerbecause the proper time for applying the fertilizer to his crops is athand. However, the combination of the facts certainly allows for thereasonable inference that a very high probability exists that Suspect isinvolved in criminal activity. Thus, analyst 300 or analyst 302 woulddirect other law enforcement personnel to investigate Suspect further todetermine if Suspect is actually involved in criminal activity. IfSuspect were engaged in criminal activity, then a bomb attack could bethwarted if either analyst 300 or analyst 302 could make the inference.

However, the above-described scenario is very simplistic because thisscenario assumes that analyst 300 and analyst 302 received andconsidered the relevant information in the first place. Because theamount of information available to be analyzed is nearlyincomprehensibly vast, neither analyst may have had their attentiondrawn to any of the facts described above. Thus, the likelihood is highboth analysts would be oblivious to the potential threat posed bySuspect. The information necessary to make the inference that Suspect isa threat does exists however, finding that information and then makingthe proper inference is comparable to finding two needles in millions ofdifferent kinds of haystacks, all moving at a high rate of speed.

FIG. 4 is a block diagram illustrating a central database used foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. The method shown in FIG. 4 can beimplemented by one or more users using one or more data processingsystems, such as server 104, server 106, client 110, client 112, andclient 114 in FIG. 1 and data processing system 200 shown in FIG. 2,which communicate over a network, such as network 102 shown in FIG. 1.Additionally, the illustrative embodiments described in FIG. 4 andthroughout the specification can be implemented using these dataprocessing systems in conjunction with central database 400.

FIG. 4 shows a solution to the problem of allowing different analysts toboth find and consider relevant information from a truly massive amountof divergent data. Central database 400 allows analyst 300 and analyst302 to find relevant information based on one or more queries and, moreimportantly, cause central database 400 to assign probabilities to thelikelihood that certain inferences can be made based on the query. Theprocess is massively recursive in that every piece of information addedto the central database causes the process to be re-executed. Anentirely different result can arise based on new information.Information can include the fact that the query itself was simply made.Information can also include the results of the query, or informationcan include data from any one of a number of sources.

Additionally, central database 400 receives as much information aspossible from as many different sources as possible. Thus, centraldatabase 400 serves as a central repository of information from analyst300, analyst 302, source 304, source 306, source 308, source 310, source312, source 314, source 316, and source 318. In an illustrativeembodiment, central database 400 can also input data into each of thosesources. Arrows 402, arrows 404, arrows 406, arrows 408, arrows 410,arrows 412, arrows 414, arrows 416, arrows 418, and arrows 420 are allbidirectional arrows to indicate that central database 400 is capable ofboth receiving and inputting information from and to all sources ofinformation. However, not all sources are necessarily capable ofreceiving data; in these cases, central database 400 does not attempt toinput data into the corresponding source.

Continuing the example regarding Suspect, either or both of analyst 300or analyst 302 could have made the inference that Suspect was possiblyengaged in criminal activity by submitting queries to central database400. Thus, the odds of thwarting an attack by Suspect are greatlyincreased by the mechanisms and methods of the illustrative embodiments.

Central database 400 is adapted to receive a query regarding a fact, usethe query as a frame of reference, use a set of rules to generate asecond set of rules to be applied when executing the query, and thenexecute the query using the second set of rules to compare data incentral database 400 to create probability of an inference. Theprobability of the inference is stored as additional data in thedatabase and is reported to the analyst or analysts submitting thequery.

Thus, continuing the above example, analyst 300 submits a query tocentral database 400 to compare known bomb makers to explosive materialpurchases. Central database 400 uses these facts or concepts as a frameof reference. A frame of reference is an anchor datum or set of datathat is used to limit which data are searched in central database 400.The frame of reference also helps define the search space. The frame ofreference also is used to determine to what rules the searched data willbe subject. Thus, when the query is executed, sufficient processingpower will be available to make inferences.

The frame of reference is used to establish a set of rules forgenerating a second set of rules. For example, the set of rules could beused to generate a second set of rules that include searching allinformation related to bombs, all information related to bomb makers,and all information related to purchases of explosive materials and bombmaking materials, but no other information. The first set of rules alsocreates a rule that specifies that only certain interrelationshipsbetween these data sets will be searched.

The database uses the second set of rules when the query is executed. Inthis case, the query compares the relevant data in the described classesof information. In comparing the data from all sources, the querymatches purchases of explosive materials to known bomb makers. Centraldatabase 400 then produces a probability of an inference. The inferenceis that Suspect has purchased 1000 pounds of high nitrate fertilizer, aknown explosive. Possibly thousands of other inferences matching otherbomb makers to purchases of explosives are also made. Thus, the analystdesires to narrow the search because the analyst cannot pick out theinformation regarding Suspect from the thousands of other inferences.

Continuing the example, this inference and the probability of inferenceare re-inputted into central database 400 and an additional query issubmitted to determine an inference regarding a probability of criminalactivity. Again, central database 400 establishes the facts of the queryas a frame of reference and then uses a set of rules to determineanother set of rules to be applied when executing the query. This time,the query will compare criminal records and group affiliations of allbomb makers identified in the first query. The query will also comparethe various identified bomb making materials and their ability to damagebuildings, where the identified bomb making materials have beenpurchased in the identified amounts over a period of time. Thus, ifSuspect purchased 100 pounds of high nitrate fertilizer ten times in tendays, this fact could be inferred.

The query is again executed using the second set of rules. The querycompares all of the facts and creates a probability of a secondinference. In this illustrative example, the probability of a secondinference is that a chance between 85 percent and 99 percent exists thatSuspect is engaged in a plot to bomb buildings. Analyst 300 then usesthis inference to direct law enforcement, military, or other relevantpersonnel to further investigate Suspect.

Thus, central database 400 includes one or more divergent data. Theplurality of divergent data includes a plurality of cohort data. Eachdatum of the database is conformed to the dimensions of the database.Each datum of the plurality of data has associated metadata and anassociated key. A key uniquely identifies an individual datum. A key canbe any unique identifier, such as a series of numbers, alphanumericcharacters, other characters, or other methods of uniquely identifyingobjects. The associated metadata includes data regarding cohortsassociated with the corresponding datum, data regarding hierarchiesassociated with the corresponding datum, data regarding a correspondingsource of the datum, and data regarding probabilities associated withintegrity, reliability, and importance of each associated datum.

Central database 400 is described further with respect to FIG. 5 throughFIG. 19. FIG. 5 describes how central database 400 operates. FIG. 6through FIG. 17 describe additional details regarding how varioussystems in central database 400 operate. FIG. 18 describes the structureof central database 400. FIG. 19 describes the selection and processingmethods and mechanisms used by central database 400 during a querysubmitted by analysts.

FIG. 5 is a block diagram of a data processing network used inconjunction with a central database for identifying past, present, orfuture criminal activity, in accordance with an illustrative embodiment.Data processing network 500 can be one or more of a vast number of dataprocessing systems, such as server 104, server 106, client 110, client112, and client 114 in FIG. 1, and data processing system 200 shown inFIG. 2. These data processing systems can communicate over a network,such as network 102 shown in FIG. 1. Central database 400 in FIG. 4communicates back and forth with data processing network 500. Centraldatabase 400 is accessed using selection and processing rules,represented by System M 502. Queries and possibly additional informationare submitted by analyst 300 or analyst 302, shown in FIG. 3, as shownby arrows 402 and 404.

Data processing network 500 includes a number of different systems, eachof which performs different functions. Each system shown can be one ormore data processing systems connected via a network, as describedabove. Each system shown in data processing network 500 can also be oneor more hardware systems or software programs adapted to perform thefunctions associated with the corresponding system. More or differentsystems than those shown can exist in data processing network 500. Thoseshown are only examples of systems that describe the functions ofcentral database 400.

Examples of systems include system A 504, system B 506, system C 508,system D 510, system E 512, system F 514, system G 516, system H 518,system 1520, system J 522, system K 524, and system L 526. Additionally,System M 502 can itself be considered a system, designated system M 502.System M 502 is described in more detail with respect to FIG. 19.

System A 504 is a system for identifying sources of data containing dataof interest. System B 506 is a system for classifying sources of dataand for recording metadata regarding the sources. As described below,central database 400 stores all data at the finest level possible, knownas individual datum, and associates metadata and an identification keywith each datum. System B 506 is the system that deals with thisfunction.

System C 508 is a system for categorizing data of interest by type.System D 510 is a system for making data addressable. System E 512 is asystem for categorizing data by availability. System F 514 is a systemfor categorizing data by relevance. System G 516 is a system forcategorizing data by integrity. System H 518 is a system for creatingcohorts. System 1520 is a system for creating relationships amongcohorts. A cohort is a group of associated individuals or objects. Acohort can be treated as a single entity; thus, central database 400 caneffectively find cohorts of interest. Additional queries can “drilldown” and find sub-cohorts of further interest. The process isrepeatable until specific individuals or objects are found.

System J 522 is a system for categorizing data by importance. System K524 is a system for assigning probabilities to inferences and assigningprobabilities to the trustworthiness, reliability, importance, andintegrity of individual datum. System L 526 is a system for categorizingdata by the source of the data.

FIG. 6 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 6 illustratesadditional details regarding system A 504 in FIG. 5. System A 504 ofFIG. 6 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system A 504. System A 504 of FIG. 6 is used in conjunctionwith other systems and functions of data processing network 500 to allowcentral database 400 of FIG. 5 to effectively receive and processqueries to create probabilities of inferences. System A 504 includes anumber of subsystems implemented as one or more hardware or softwaresystems in one or more data processing systems.

Many data sources exist and many new data sources are created nearlycontinuously. System A 504 is used to find new and existing sources ofdata. Examples of systems for finding data of interest include webcrawlers, software or hardware for checking or updating known sources ofinformation, software or hardware for receiving user-definedinformation, software or hardware for performing data mining, and anynumber of additional sources of information.

System A 504 can receive data from various sources, such as knownsources from subsystem A 600, manual additions of information fromsubsystem B 602, automated detection of information from subsystem C604, previous searches and queries from subsystem D 606, and through thediscovery of new data sources from subsystem E 608. System A 504continually checks for new data sources and updates to known datasources.

FIG. 7 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 7 illustratesadditional details regarding system B 506 in FIG. 5. System B 506 ofFIG. 7 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system B 506. System B 506 of FIG. 7 is used in conjunctionwith other systems and functions of data processing network 500 to allowcentral database 400 of FIG. 5 to effectively receive and processqueries to create probabilities of inferences. System B 506 includes anumber of subsystems implemented as one or more hardware or softwaresystems in one or more data processing systems.

System B 506 classifies sources and records metadata regarding eachsource. Classification of sources into various levels of classificationsassists central database 400 in FIG. 4 and data processing network 500in FIG. 5 to effectively group information together. To further thesefunctions, subsystem A 700 performs source profiling. Source profilingincludes one or more of describing the location of the source ofinformation, the trustworthiness of the source, the reliability of thesource, the integrity of the source, the time the source was available,the time the source was last updated, contact information regarding thesource, or many other types of information regarding the source of data.

System B 506 also includes subsystem B 702 for performing data miningand clustering of data source content. Subsystem B 702 allows system B506 to mine data from various sources and then cluster the dataaccording to various parameters, such as data source, data type, timestamps associated with the data, data having similar subject matter,data category, and many other subjects about which data can beclustered. System B 506 also includes subsystem C 704 for catalogingdata within a source into metadata. This software or hardware allowssystem B 506 to establish metadata for each datum and associate themetadata with the datum.

An example of software that can implement system B 506 is theUnstructured Information Management Architecture (UIMA) platformavailable from International Business Machines corporation of Armonk,N.Y. UIMA can also be implemented as hardware. Clustering can also beperformed using a clustering algorithm, Baysian statistics, user-definedrules, or combinations of these techniques.

FIG. 8 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 8 illustratesadditional details regarding system C 508 in FIG. 5. System C 508 ofFIG. 8 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system C 508. System C 508 of FIG. 8 is used in conjunctionwith other systems and functions of data processing network 500 to allowcentral database 400 of FIG. 5 to effectively receive and processqueries to create probabilities of inferences. System C 508 includes anumber of subsystems implemented as one or more hardware or softwaresystems in one or more data processing systems.

System C 508 categorizes data of interest by type. System C 508 includessubsystem A 800 for performing ontology and taxonomy processing of datain order to categorize data of interest by type. Subsystem B 802 alsocategorizes data of interest by type according to open source, publicdomain, and industry standards. Additionally, subsystem C 804categorizes data of interest by type according to hierarchies of dataand data types established in system B 506.

FIG. 9 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 9 illustratesadditional details regarding system D 510 in FIG. 5. System D 510 ofFIG. 9 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system D 510. System D 510 of FIG. 9 is used in conjunctionwith other systems and functions of data processing network 500 to allowcentral database 400 of FIG. 5 to effectively receive and processqueries to create probabilities of inferences. System D 510 includes anumber of subsystems implemented as one or more hardware or softwaresystems in one or more data processing systems.

System D 510 makes data addressable. Addressability of data allows datato be stored at an atomic level. Such data is considered atomic data.Atomic data is data stored at the finest possible degree of granularity.Thus, for example, data regarding a person is not necessarily storedunder a person's name. Instead, data regarding the person is storedseparately as name, address, phone number, and other informationregarding the person. Each fact is stored as an individual datum.Metadata associated with each datum allows central database 400 in FIG.4 and data processing network 500 in FIG. 5 to associate a number ofindividual data with each other in order to build a profile of theperson.

The profile of the person could be considered a cohort. Cohorts aregroups of objects or people that share common characteristics or areotherwise part of a group. Thus, the name, address, phone number, andother information regarding an individual can be associated with thatindividual. The cohort is the individual in that all of the individualfacts regarding the individual are associated with that individual.

Making atomic data addressable is a non-trivial task, because most datareceived at central database 400 in FIG. 4 or data processing network500 in FIG. 5 is not atomic and is not easily addressable. Thus, systemD 510 includes subsystem A 900 for converting text to data. Similarly,system D 510 includes subsystem B 902 for addressing text data derivedfrom subsystem A 900. System D 510 also includes subsystem C 904 forrecognizing and decoding encrypted data. If the data cannot bedecrypted, then subsystem C 904 can recognizing encrypted data and storethe fact that the encrypted data exists, along with any informationknown about the encrypted data, such as source, time of creation, timeof entry, encryption method if known, or other information.

Additionally, system D 510 includes subsystem D 906 for converting voiceor image files to text, and from there converting text to data.Subsystem B 902 can then allow such data generated in subsystem D 906 tobe made addressable at the atomic level.

FIG. 10 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 10 illustratesadditional details regarding system E 512 in FIG. 5. System E 512 ofFIG. 10 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system E 512. System E 512 of FIG. 10 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System E 512includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

System E 512 categorizes data by availability. Data might be recognized,but not necessarily available. Data should be categorized byavailability in order to understand the context of data that isavailable. Thus, for example, system E 512 includes subsystem A 1000 fordetermining and recording whether data is secret data. Similarly, systemE 512 includes subsystem B 1002 for determining the periodicity of dataavailability. Some data may be available at only particular times ortime intervals. Similarly, system E 512 includes subsystem C 1004 foridentifying and recording restriction of access to data and subsystem E1008 for identifying and recording the encryption of data.

System E 512 also includes subsystem D 1006 determining whether datashould be federated or accessed via extract, transform, and load (ETL)techniques. The decision of whether data should be made available viafederation or extract, transform, and load techniques can be important.Federated access to data is made by accessing desired data piecemeal.Extract, transform, and load techniques allow access to data byextracting, transforming, and loading all data onto a local network ordata processing system.

For example, a large database is stored at a building maintained by theFederal Bureau of Investigation. A remote computer can access thedatabase over a network via a query to determine various informationabout a known suspect. This type of access to the data in the databaseis federated data access. On the other hand, the entire database couldbe extracted, transformed, and loaded onto what was the remote computeror remote network. The formerly remote computer can now access theinformation about the known suspect directly without accessing thedatabase stored at the building maintained by the Federal Bureau ofInvestigation.

The decision as to whether efficient access to data is accomplished viafederation or extract, transform, and load techniques can be difficult.Techniques for efficiently making this decision are found in ourdisclosure identified by application Ser. No. 11/416,973 filed on May 2,2006.

FIG. 11 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 11 illustratesadditional details regarding system F 514 in FIG. 5. System F 514 ofFIG. 11 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system F 514. System F 514 of FIG. 11 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System F 514includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

System F 514 categorizes data by relevance. System F 514 includessubsystem A 1100 for determining a quality of a source of data andcategorizing the data from that source based on the quality of thesource. The quality of the source of data has an impact on the relevanceof the data in that low quality data will be less relevant. Low qualitydata is less relevant because the data is less reliable, and data thatis less reliable is less relevant. The quality of the source of data canbe implemented quantitatively, through assigning a number scale to thequality of data, or qualitative, as in assigning a quality level such as“low,” “medium,” and “high.” Data can be categorized by quality; thus,data of a given quality from a number of different sources can becategorized together.

System F 514 also includes subsystem B 1102 for determining therelevance of data through a perceived relevance for the purpose of agiven query or a type of query and then categorizing the data byperceived relevance. Perceived relevance can be provided by a userthrough the form of a numerical value or a relative value. Perceivedrelevance can also be provided automatically by the database, hardware,or software according to rules established in the query or query type.Data assigned to a particular perceived relevance level can becategorized together.

System F 514 also includes subsystem C 1104 for determining therelevance of non-current data and categorizing data by whether the datais non-current. Some data become less relevant over time. For example,World War II intelligence data from the year 1943 regarding Nazi Germanmilitary personnel records is not likely to be relevant to modernintelligence investigations. However, no data is truly useless orobsolete in the database and methods described herein. Thus, such datais stored. To account for the fact that the data is old, the data isassigned less relevance via the use of metadata.

Less relevant data may become relevant under certain circumstances. Therelevance of non-current data can also change. For example, if the WorldWar II intelligence data from above leads to an inference that astill-living suspected Nazi war criminal might be living in a particularcountry, then the data becomes more pertinent to the intelligenceinvestigation. In this case, the relevance of the non-current dataincreases.

Additionally, non-current data includes data that has less relevanceafter a given event. For example, data regarding threats to bomb asporting event become less relevant after the sporting event takes placewithout incident. However, such data is not obsolete or useless, even ifit is less relevant as being non-current.

Whatever the source or reason for being non-current, data at a givenlevel of being non-current can be categorized together. Thus, forexample, non-current data regarding threats against a completed sportsevent could, theoretically, be categorized together with the World WarII intelligence data above, at least according to the degree to whicheach set of data is non-current.

FIG. 12 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 12 illustratesadditional details regarding system G 516 in FIG. 5. System G 516 ofFIG. 12 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system G 516. System G 516 of FIG. 12 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System G 516includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

System G 516 includes subsystem A 1202 for categorizing data byintegrity. System G 516 includes software or hardware for analyzing dataintegrity by self-integrity and system integrity. Subsystem A 1202allows the central database to check the degree of self-integrity ofreceived data. Data integrity is the quality of correctness,completeness, wholeness, soundness, and compliance with the intention ofthe creators of the data. Data integrity is achieved by preventingaccidental or deliberate but unauthorized insertion, modification, ordestruction of data in a database. Thus, data has a degree ofself-integrity according to the degree of the integrity of the data.Data can be categorized according to a given degree of integrity. Thedegree of integrity can be quantitative, through the use of a numericalscoring system, or qualitative, such as assigning qualitativeassessments of data integrity including “low,” “medium,” and “high.”

System G 516 also includes subsystem C 1206 for determining theprobability of a correct analysis of a given system based on theintegrity of the data. Data having less integrity is less likely toresult in an inference with a high probability of correctness.

System G 516 also includes subsystem D 1208 for assigning confidence inan analysis by the integrity of the data. Subsystem D 1208 is differentthan subsystem B 1204 in that the probability of correct analysis can beestimated according to the data integrity before the actual analysistakes place. This confidence in analysis can also be used whencategorizing data by integrity.

FIG. 13 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 13 illustratesadditional details regarding system H 518 in FIG. 5. System H 518 ofFIG. 13 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system H 518. System H 518 of FIG. 13 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System H 518includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

FIG. 13 includes software or hardware for creating cohorts. A cohort isa group of associated individuals or objects. A cohort can be treated asa single entity when performing analysis. For example, a cohort can be“all individuals who have received flight instruction.” This set ofindividuals, or cohort, is treated as a single data point duringanalysis. If more detail is desired, than specific individuals in thecohort or sub-cohorts can be identified and/or searched. A sub-cohort isa cohort; however, a sub-cohort can be said to exist within the domainof a larger cohort. In this example, a sub-cohort could be “allindividuals who have a commercial flying license.”

System H 518 includes subsystem A 1300 for clustering data into cohortsusing source data. Through subsystem A 1300 the database canautomatically generate cohorts and sub-cohorts using data stored at anatomic level. Atomic data is data stored at the finest possible degreeof granularity. Thus, this process of generating cohorts is powerful inthat cohorts can be generated involving any given individual type ofdata. For example, individuals need not be associated into a cohort inorder to associate phone numbers into a cohort. A group of phone numberscan be generated into a cohort according to any parameter, such as, forexample, area code. A group of individuals can be in one cohort, a groupof phone numbers in a set of area codes can be in another cohort, and agroup of individuals having commercial flying licenses can be in a thirdcohort. A fourth cohort can be automatically generated that representsall individuals in the first cohort having commercial flying licensesand having phone numbers in a particular area code.

System H 518 also includes subsystem B 1302 for receiving manuallycreated cohorts. Subsystem B 1302 allows one or more users to manuallycreate a cohort. A cohort can be manually created by inputting a commandto the central database or other software or hardware. The command canbe to associate one set of data with another set of data. For example, auser can input a command to associate “people” with “commercial flyinglicenses” to create a cohort of “people with commercial flyinglicenses.” Central database 400 in FIG. 4 allows this command to beexecuted successfully.

The cohorts themselves, however, are generated and stored as data in thedatabase. Thus, each generated cohort becomes a new datum for use incentral database 400 in FIG. 4.

FIG. 14 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 14 illustratesadditional details regarding system 1520 in FIG. 5. System 1520 of FIG.14 can be implemented via one or more data processing systems connectedby a network, as described in FIGS. 1 and 2, or via one or more hardwaresystems or software programs that can perform the functions of system1520. System 1520 of FIG. 14 is used in conjunction with other systemsand functions of data processing network 500 to allow central database400 of FIG. 5 to effectively receive and process queries to createprobabilities of inferences. System 1520 includes a number of subsystemsimplemented as one or more hardware or software systems in one or moredata processing systems.

System 1520 includes hardware or software for creating relationshipsamong cohorts. Relationships among cohorts can be any relationship. Anexample of a relationship between cohorts is the association of a firstcohort as a sub-cohort of a second cohort. Cohorts can be associatedwith each other according to mathematical set theory. Cohorts can alsobe associated with each other according to user-defined associations,such as, for example, associating two cohorts as being weakly orstrongly associated with each other.

System 1520 includes subsystem A 1400 for manually creatingrelationships between cohorts. Thus, users can use hardware or softwareto create relationships between cohorts for use by central database 400in FIG. 4. Additionally, system 1520 includes subsystem B 1402 forcrating relationships among cohorts by frames of reference.Relationships among cohorts can be associated according a frame ofreference in that a frame of reference serves as an anchor forgenerating associations among cohorts.

For example, a frame of reference can be a fact that a known terroristhas just obtained a commercial flying license. Subsystem B 1402 cangenerate relationships among existing or new cohorts using this frame ofreference. For example, a first cohort is “all individuals withcommercial flying licenses.” A second cohort is “all known individualsknown to associate with the known terrorist.” A relationship betweenthese two cohorts can be generated. The relationship between these twocohorts is created by the frame of reference that a known terrorist hasobtained a commercial flying license.

FIG. 15 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 15 illustratesadditional details regarding system J 522 in FIG. 5. System J 522 ofFIG. 15 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system J 522. System J 522 of FIG. 15 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System J 522includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

System J 522 includes hardware or software for categorizing data byimportance. The importance of a given datum is determined qualitativelyby a user, but can be assigned a quantitative or qualitative value bythe user for use by central database 400 in FIG. 4. System J 522includes subsystem A 1500 for determining the level of a threat. Thelevel of a threat reflects the seriousness of a threat or crime, asdetermined by a user. For example, detonation of a nuclear bomb isconsidered a very serious threat. Note that the reliability of a tipthat a nuclear bomb is going to be detonated in a city is factored intosystem F 514 in FIG. 5, in which data is categorized by relevance. If aperson under the influence of hallucinogenic drugs provides the nucleardetonation tip and that individual has no reason to have informationregarding nuclear weapons, then the information has a low degree ofreliability and thus a low degree of relevance. These two factors, levelof threat and relevance (reliability) are taken into account whencalculating the probability of an inference.

Once the importance of a datum is determined, system J 522 allows datato be categorized by importance. Thus, data having a particular degreeof importance can be grouped together.

System J 522 also includes subsystem B 1502 for calculating or receivinginput regarding political importance of a particular datum. For example,a particular crime might be receiving much public attention.Politically, authorities desire to give the investigation of the crimehigher importance. This fact can be factored into account usingsubsystem B 1502. For example, subsystem B 1502 can raise the relevanceof a particular fact regarding a person if that person is somehowconnected to the crime as a witness.

System J 522 also includes subsystem C 1504 for creating user-definedimportance. Thus, a user can establish an importance of a fact. A usercan also establish a range of values of importance within which centraldatabase 400 in FIG. 4 can adjust a given importance of that fact. Avalue of importance can be quantitative, in terms of a number valueassigned to importance, or qualitative in terms of relative values.

FIG. 16 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 16 illustratesadditional details regarding system K 524 in FIG. 5. System K 524 ofFIG. 16 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system K 524. System K 524 of FIG. 16 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System K 524includes a number of factors determined by one or more hardware orsoftware systems in one or more data processing systems.

System K 524 assigns probabilities to inferences. An inference might bedrawn based on comparing data in the database, but the inference mightbe strong or weak. The strength of an inference is described in terms ofa probability. The probability reflects the relative percentage chancethat the inference is true.

Many factors influence the probability of one or more inferences.Examples of factors include factor A 1600, timing; factor B 1602,source; factor C 1604, trustworthiness; factor D 1606, relevance; factorE 1608, reliability; factor F 1610, importance; factor G 1612, dataintegrity; and factor H 1614, cohort information. Many of these factorsare determined using other systems, such as system A 504 through systemJ 522.

Factor A 1600, timing, can influence the probability that an inferenceis true in that the temporal relationship between facts can have asignificant impact on the likelihood of a correct inference. Forexample, referring to the example of World War II Nazi Germanintelligence above, the fact that the intelligence is very old candecrease the probability that an inference drawn based on thatinformation is true. In contrast, information that a known bomberpurchased excessive or illegal explosives one day before a majorterrorist event would increase the probability of an inference that theknown bomber perpetrated the terrorist event.

Factor B 1602, source, can also influence the probability that aninference is true. If the source of information is a known drug addictconvicted of multiple counts of felony perjury, then a decrease resultsin the probability that an inference drawn from information from theknown drug addict is true. Similarly, information garnered from a randomInternet Web site is probably less likely to be true, though informationgathered from a known expert in a field is more likely to be true. Thus,the source of the information influences the probability that aninference is true or false.

Factor C 1604, trustworthiness also influences the probability that aninference is true. Trustworthiness can be related to source, timing,reliability, and other factors. However, a user or the hardware orsoftware can assign an independent separate trustworthiness score,either quantitative or qualitative, to a set of data. Thus, for example,a user or the hardware or software could increase the probability thatthe known drug user is providing trustworthy information based onprevious specific information from that known drug user or based oncorroborative evidence.

Factor D 1606, relevance, can also influence the probability that aninference is true. Information that a country in Africa recentlydeclared war on a country in Asia probably has little relevance towhether or not a domestic terrorist in the United States is plotting tobomb a domestic target in the United States. That information mightstill be considered, though the probability that the two facts arerelated is small given that they do not seem relevant to each other.Thus, probability of an inference that a domestic terrorist is plottinga domestic bombing is relatively low in view of the fact of thedeclaration of war. In turn, the probability of such an inferenceincreases in view of a different fact with higher relevance, such as,for example, if the domestic terrorist is discovered to be associatingwith a close group of other known bombers.

Factor E 1608, reliability, can also influence the probability that aninference is true. Reliability of data can be influenced by a number ofthe other factors described in relation to system K 524. Like factor C1604, trustworthiness, however, a user or hardware or software canassign an independent reliability score, quantitative or qualitative,that indicates the reliability of data.

Factor F 1610, importance, can also influence the probability that aninference is presented to a user. Although the importance of a fact doesnot necessarily translate to the correctness of the fact, the importanceof the fact can influence whether a user is presented with theprobability of truth of an inference drawn based on that fact.

Thus, for example, the known drug user described above providesinformation relating to a plot to assassinate a political figure. Thisplot is considered important. Although the source is consideredunreliable, thereby reducing the probability of an inference that theplot is true, the fact that the plot is considered important increasesthe probability that the inference will be presented to the user.

Additionally, factor G 1612, data integrity, can influence theprobability that an inference is true. Data that is considered to havegood integrity is more likely to be correct, reliable, and trustworthy.Hence, data with good integrity can increase the probability that aninference drawn on that data is true. In contrast, data that does nothave good integrity has the opposite effect—to decrease the probabilitythat an inference drawn on that data is true.

Additionally, factor H 1614, cohort information, can influence theprobability that an inference is true. For example, the domesticterrorist described above is associated with a cohort that is made up ofknown bombers. The fact that the known bomber can be associated inrecent time with the cohort increases the probability of truth of theinference that the domestic terrorist is engaged in terrorist activity.

Additionally, each of the factors 1602 through 1614 can have synergisticeffects on the total probability that an inference is true. Thus, theprobabilities are not necessarily linearly additive. Considered as awhole, several facts considered together could synergistically increaseor decrease the total probability that an inference is true. In otherwords, in terms of assigning probabilities to inferences, the whole ofall information is greater than the sum of the bits of information thatmake up the whole. Furthermore, each of the factors 1602 through 1614can be modified by a user or hardware or software via independent scoresassociated with a corresponding factor.

FIG. 17 is a block diagram illustrating functions of a data processingsystem used with a data processing network and a central database foridentifying past, present, or future criminal activity, in accordancewith an illustrative embodiment. Specifically, FIG. 17 illustratesadditional details regarding system L 526 in FIG. 5. System L 526 ofFIG. 17 can be implemented via one or more data processing systemsconnected by a network, as described in FIGS. 1 and 2, or via one ormore hardware systems or software programs that can perform thefunctions of system L 526. System L 526 of FIG. 17 is used inconjunction with other systems and functions of data processing network500 to allow central database 400 of FIG. 5 to effectively receive andprocess queries to create probabilities of inferences. System L 526includes a number of subsystems implemented as one or more hardware orsoftware systems in one or more data processing systems.

System L 526 categorizes data by source. As described above, the sourceof data can influence the probability of truth of an inference drawnfrom that data. Additionally, the category of data can, itself, be usedto draw inferences.

Thus, system L 526 includes subsystem A 1700 for organizing dataaccording to source organization. A source organization is theorganization that derived the data or from which the data was received.Examples of source organizations include federal and state agencies,corporations, religious institutions, and others. System L 526 alsoincludes subsystem B 1702 for organizing data by history of physicalsources. A history of physical sources is a chain of computers on whichdata was stored. For example, if data was generated on computers Athrough G, transferred to computers H through W, and finally transferredto computers X through Z, then the history of physical sources wouldinclude all of computers A through Z in the provided order at theprovided times.

System L 526 includes subsystem C 1704 for organizing data according towho provided the data. Thus, the source of data can be categorized notonly by organization but also by individual, cohorts of individuals, andcohorts of organizations.

System L 526 also includes subsystem D 1706 for organizing dataaccording to a history of who held data. A history of who held data issimilar to a chain of custody in that the history of who held data is alisting of the order in which individuals, organizations, or cohortsheld particular data at particular times.

FIG. 18 is a block diagram of illustrating components and operatingcharacteristics of a central database for identifying past, present, orfuture criminal activity, in accordance with an illustrative embodiment.Central database 400 of FIG. 18 is central database 400 of FIG. 5.Central database 400 can be implemented via one or more data processingsystems connected by a network, as described in FIGS. 1 and 2, or viaone or more hardware systems or software programs that can perform thefunctions of central database 400. Central database 400 of FIG. 18 isused in conjunction with other systems and functions of data processingnetwork 500 to allow central database 400 to effectively receive andprocess queries to create probabilities of inferences.

FIG. 6 through FIG. 17 describe characteristics of data processingnetwork 500 in terms of what data processing network 500 does. FIG. 18describes, together with FIGS. 20A and 20B, what central database 400is.

In particular, central database 400 has a number of characteristics.Characteristic A 1800 is that all data is tagged with time stamps. Thisproperty means that each datum is associated with metadata that reflectsa time stamp as to when the datum was received in the database. A timestamp can also include additional times, such as when a datum wascreated, when a datum was modified, and when a datum was accessed.Additional data can be used to indicate when a datum was deleted.

Characteristic B 1802 is that data is stored at an atomic level. Datastored at an atomic level is data stored at the finest possible degreeof granularity. Thus, for example, data regarding a person is notnecessarily stored under a person's name. Instead, data regarding theperson is stored separately as name, address, phone number, and otherinformation regarding the person. Each fact is stored as an individualdatum. Metadata allow central database 400 to create a profile of theperson associated with the name.

Characteristic C 1804 is that the levels of granularity of the data areconformed to the dimensions of the database. Not all data is stored atthe finest degree of granularity. The degree of granularity is thedegree to which data can be stored at an atomic level. While data isstored at the finest degree of granularity possible, some data must bestored at higher degrees of granularity because of how the data wasgenerated or discovered. However, no matter what the degree ofgranularity of data, all data is conformed to the dimensions of thedatabase.

The term “data is conformed to the dimensions of the database” meansthat, for the entire database, any dimension will have the same meaningto all data elements that use the dimension as a reference.Specifically, data is conformed to the dimensions of the database whentwo dimensions share the same foreign key. A foreign key is an objectthat links data to a dimension. Thus, all data elements that share thesame foreign key have the same frame of reference.

Characteristic D 1806 is that hierarchy is determined by thedimensionality of the database. As described above, all data conform tothe dimensions of the database. In this way, a hierarchy of data can beestablished for each characteristic of a datum.

For example, because the data conform to the dimensions of the database,all data elements that reference a location dimension will “perceive”the location in the same way. The same location could be shared bydifferent events and people. By conforming the data to the dimensions ofthe database, a query can be made to ask what other events areassociated with the particular location. Additionally, a query can bemade to ask what other events are associated with other locations withina hierarchy of locations. For example, an event may occur at a homeaddress, and the hierarchy of locations could be a block, a city, acommercial zone, a county, a congressional district, a state, a country,or any other means for denoting locations within a hierarchy associatedwith a particular location.

Characteristic E 1808 is that data is tagged by the source of the data.Thus, for example, each datum in the database has associated with itmetadata that tags the datum by the source of the data. In this way, theidentity, and possibly other characteristics such as location, andcontact information of the source of each datum is known and can bequeried.

Characteristic F 1810 is that data is tagged by channel. A channel isthe method by which data is obtained. For example, if data is downloadedvia the Internet, then the channel is the Internet network and thesource is the host data processing system. If data is received in theform of pictures delivered by courier, then the channel is hand deliveryby the courier. In any case, data regarding the channel is tagged asmetadata associated with the corresponding datum.

Characteristic G 1812 is that data is tagged by location. Thischaracteristic means that each datum is associated with metadata thatincludes information regarding the location of where the data is stored.Central database 400 can be extremely large, tens of thousands ofterabytes and possibly vastly more, and spread across numerous dataprocessing systems and storage devices. To facilitate the location ofdata, each datum is associated with metadata that indicates the locationof the data.

Characteristic H 1814 is that all cohorts are maintained in centraldatabase 400. Cohorts are groups of objects or people that share commoncharacteristics or are otherwise part of a group. Each cohort is,itself, stored as data in central database 400. Thus, once a cohort isgenerated, that cohort remains permanently in central database 400 forfurther reference and comparison.

Characteristic I 1816 is that events are modeled as inverted starschemas. A star schema (sometimes referenced as star join schema) is thesimplest data warehouse schema, including a single fact table with acompound primary key, with one segment for each dimension and withadditional columns of additive, numeric facts. The star schema makesmulti-dimensional database (MDDB) functionality possible using atraditional relational database. Fact tables in star schema are mostlyin third normal form (3NF), but dimensional tables are in de-normalizedsecond normal form (2NF). Normalized dimensional tables look likesnowflakes.

In an inverted star schema, a star schema or a constellation of starschemas can be viewed from any point. Thus, a command can be issued to adatabase to refold and refocus the database, mathematically speaking,with respect to a particular point in the star schema. No true physicaltransformation of the database need occur.

In an illustrative example, a star schema database relates a businesstransaction with a sale in the center, a merchant connected to the saleon the right side and a buyer connected to the sale on the left side. Inan inverted star schema, the database is refolded and refocused suchthat the merchant is the center of the star schema. Similarly, the buyercould be made the center of the star schema.

FIG. 19 is a block diagram illustrating subsystems for selection andprocessing of data using a central database for identifying past,present, or future criminal activity, in accordance with an illustrativeembodiment. Each subsystem shown in FIG. 19 can be implemented using oneor more hardware or software components in one or more data processingsystems. In some embodiments, more than one subsystem can be implementedusing the same hardware or software. Each subsystem shown in FIG. 19describes a function or action that occurs during selection andprocessing in system M 502 of FIG. 5.

Subsystem A 1900 crawls text. The term “crawl text” means that text issearched for words, characters, or strings of information. Optionally,during text crawling, text is parsed into words, characters, or stringsand stored for future use. During selection and processing text relatedto or retrieved for a query that has not already been crawled can becrawled. Additionally, text entered as part of a query can be crawled.

Subsystem B 1902 generates and stores summaries of a query, results of aquery, or intermediate results of a query. Summaries can be presented toa user in various different form, such as text, charts, graphs, images,or voice text, for subsequent analysis. Similarly, subsystem C 1904stores each query. Thus, every query made to central database 400becomes part of the data stored in central database 400.

Subsystem D 1906 defines and tags extracted or derived data. Dataextracted or derived during selection and processing of queries or datais defined and tagged as part of the query or selection and processingprocess. Thus, additional metadata can be added to each datum extractedor derived during selection and processing. Similarly, additional datacan be created during selection and processing.

Subsystem E 1908 relates events and multiple hierarchies. Subsystem E1908 uses inverted star schemas to relate a particular event to otherrelated data. For example, an event can be related to a personassociated with the event. However, because the dimensions of the dataconform to the database and because all data are associated withhierarchies, the person can be associated with groups of people. Forexample, a particular suspect could be associated with a criminalorganization. Thus, subsystem E 1908 allows the database to relate theparticular event to the suspect and also to the criminal organization towhich the suspect belongs. In other words, events are related tomultiple hierarchies.

Subsystem F 1910 analyzes past data to identify current relevance.Non-current data, such as data described subsystem C 1104 of FIG. 11,could possibly be relevant to a current situation; thus, non-currentdata and past data is analyzed along with current data. Subsystem F 1910analyzes the past data to identify any current relevance that mightexist. Not all non-current data in central database 400 is necessarilyanalyzed; instead, only non-current data related to the query isanalyzed in order to conserve processing overhead.

Subsystem G 1912 receives and updates data annotated by a systemanalyst, or user. Thus, a user can update data or metadata in centraldatabase 400.

Subsystem H 1914 assigns probabilities to inferences and probabilitiesto the trustworthiness and reliability of data. Subsystem H 1914compliments, or may be part of system G 516 of FIG. 12 or system K 524of FIG. 16. However, subsystem H 1914 can operate independently of thesesystems during selection and processing of queries in order to dividethe processing resources used to execute a query and continually updatecentral database 400. However, subsystem H 1914 operates in a mannersimilar to system G 516 and system K 524.

Subsystem 11916 assigns a category to a probability generated bysubsystem H 1914. Probabilities are categorized by fact, inference,trustworthiness, reliability, from which source a fact was derived, andmany other categorizations.

Subsystem J 1918 identifies new cohorts. Identification of new cohortsis a valuable part of selection and processing of a query. New cohortsare identified by comparing initially unrelated data, identifyingpatterns in the initially unrelated data, and then relating that data tocreate a cohort from that data.

For example, suspect A and suspect B are both known terrorists; however,suspect A is a domestic terrorist who has previously not had arelationship with suspect B who is a foreign terrorist. During selectionand processing of a query related to a terrorist activity, system M 502identifies that suspect A and suspect B were both in a common locationwithin the same day. Subsystem J 1918 creates a new cohort including“suspect A and suspect B” based on the co-location of the suspectsclosely in time. This new cohort can be used during further selectionand processing. This new cohort may be presented to a user. The usermay, depending on circumstances, decide that suspect A and suspect B areforming a new terrorist cell. The user, though subsystem G 1912(annotation), can label the cohort including “suspect A and suspect B”as a possible new terrorist cell. This information is then included incentral database 400, whereupon selection and processing continues inorder to generate more information regarding possible activities of thepossible new terrorist cell.

Subsystem K 1920 produces a summary of results and conclusions forpresentation to a user. The summary of results can take any useful form,such as text, charts, graphs, graphics, video, audio, or other forms.The summary of results can also be modified for presentation toparticular users. For example, text can be adapted to use differentlanguages or terms of greatest usefulness to a given user.

Subsystem L 1922 identifies specific relationships from new cohorts.Using the example of suspect A and suspect B above, subsystem J 1918identified those two individuals as a new cohort. A new relationshipbetween suspect A and suspect B as superior and underling might beidentified. Additionally, a relationship between suspect A andpreviously unrelated suspect C might be established simply becausesuspect A and suspect B have been incorporated into a new cohort.

Subsystem M 1924 provides nearly continual recursion of queries. Theentire process of analysis, as shown in FIG. 22 and FIGS. 23A and 23B,is performed over and over again. During each iteration each newinference and each new probability of an inference is included incentral database 400. The addition of this new information can changethe results of the inference and the probability of the inference, andcan also generate new inferences of interest.

The process of recursion proceeds until a threshold is met. In oneexample, a threshold is a probability of an inference. When theprobability of an inference decreases below a particular number, therecursion is made to stop. In another example, a threshold is a numberof recursions. Once the given number of recursions is met, the processof recursion stops. Other thresholds can also be used.

FIGS. 20A and 20B are an exemplary structure of a database that can beused for central database 400. FIGS. 20A and 20B show entity relationdata model 2000. Entity relation data model 2000 can be created usingstandardized notation for generating representations of databasestructures for large and/or complex databases. Entity relation datamodel 2000 can be implemented as one or more databases and/orapplications in one or more data processing systems which can beconnected over a network. For example, entity relation data model 2000can be implemented using servers 104 and 106, clients 110, 112, 114,storage 108, and network 102 shown in FIG. 1.

In entity relation data model 2000 shown in FIGS. 20A and 20B, event2002 is in the center of an inverted star schema. An inverted starschema is described with respect to subsystem E 1908 in FIG. 19. Thus,other entities, such as person event 2004, event type 2006, product2008, or any other entity can be made the center of entity relation datamodel 2000. A entity is a box having a name or title outside the box,wherein a box may have a dividing line. Event 2002 contains a number ofkeys, including event key 2010 that uniquely identifies the event. Event2002 contains foreign keys associated with event 2002, including timekey 2012, date key 2014, location key 2016, organization key 2018, andsource key 2020. Thus, event 2002 can be related to time, date, locationof the event, organizations involved with the event, and the source ofwhere such data comes from. Other foreign keys can be associated withevent 2002, possibly numerous additional foreign keys. Event 2002 alsocontains details, such as event 2022, the effective date of the event2024, the date on which the event terminated 2026, and possibly otherdetails.

Other entities, such as entities 2004, 2006, 2008, and the otherentities shown in FIGS. 20A and 20B also contain similar structures.Structures include keys, foreign keys, and details or notes regardingthe event.

Entities are related to each other using the lines shown. A solid lineindicates a relationship between objects. Thus, for example, line 2028indicates a relationship between person event 2004 and event 2002.Symbol 2030 indicates the “one side” of a one to many relationship.Symbol 2032 indicates the “many side” of one to many relationship. Thus,for example, event 2002 relates to many different people, includingperson event 2004, as shown in FIGS. 20A and 20B. Other similarrelationships are shown between the various entities shown in FIGS. 20Aand 20B. Other symbols can be used. For example, symbol 2034 indicates amany to one recursive relationship among locations in location entity2036.

The illustrative entity relation model shown in FIGS. 20A and 20B isexemplary. More or fewer entities can appear in an entity relation modelused in different aspects of the methods and devices described herein.In an illustrative embodiment, a vast number of entities can exist, eachhaving vast numbers of keys, foreign keys, and associated details.

FIG. 21 is a flowchart illustrating establishment of a database adaptedto establish a probability of an inference based on data contained inthe database, in accordance with an illustrative embodiment. The processshown in FIG. 21 can be implemented using central database 400, dataprocessing network 500, and system M 502, all of FIG. 5. In anillustrative embodiment, each of central database 400, data processingnetwork 500, and system M 502 can be implemented in a single dataprocessing system or across multiple data processing systems connectedby one or more networks. Whether implemented in a single data processingsystem or across multiple data processing systems, taken together alldata processing systems, hardware, software, and networks are togetherreferred-to as a system. The system implements the process.

The process begins as the system receives the database structure (step2100). The database can have a structure similar to that shown in FIGS.20A and 20B, though the database structure can vary and is likely to bemuch more complex than the structure shown in FIGS. 20A and 20B.However, the fundamental nature of the structure is similar to thatpresented in FIGS. 20A and 20B.

Next, the system establishes a rules set for determining additional rulesets to be applied to a query (step 2102). Processing resources arelimited. Central database 400 can be extremely large and the number ofpossible interactions and relationships among all data in centraldatabase 400 can be exponentially much larger still. Thus, rules areestablished in order to limit the scope of comparison. In anillustrative example, the query or facts related to the query are usedto establish a frame of reference for the query. The frame of referenceis used to limit the scope of the query so that not all data in centraldatabase 400 need be searched and not all interactions among thesearched data need be analyzed. However, the process of establishingthose search rules should preferably be performed by the system becausethe system has all of the information useful for determining the scopeof the search, the search space, and other factors for limiting thequery. Additionally, not all users will be familiar enough with centraldatabase 400, the system, or computer programming to create a useful setof search rules. Therefore, the system establishes a set ofdetermination rules used to determine the search rules used during aquery (step 2102).

The system also receives divergent data in central database 400 (step2104). Divergent data is sets of data having different types, sizes,compatibilities, and other differences. Divergent data can be receivedfrom many different sources.

The system conforms received divergent data to the database (step 2106).As described with respect to FIG. 19 and FIGS. 20A and 20B, data isconformed to the dimensions of the database when two dimensions sharethe same foreign key. The system then stores conformed data as part ofcentral database 400 (step 2108). The process terminates thereafter.

FIG. 22 is a flowchart illustrating execution of a query in a databaseto establish a probability of an inference based on data contained inthe database, in accordance with an illustrative embodiment. The processshown in FIG. 22 can be implemented using central database 400, dataprocessing network 500, and system M 502, all of FIG. 5. In anillustrative embodiment, each of central database 400, data processingnetwork 500, and system M 502 can be implemented in a single dataprocessing system or across multiple data processing systems connectedby one or more networks. Whether implemented in a single data processingsystem or across multiple data processing systems, taken together alldata processing systems, hardware, software, and networks are togetherreferred-to as a system. The system implements the process.

The process begins as the system receives a query regarding a fact (step2200). The system establishes the fact as a frame of reference for thequery (step 2202). The system then determines a first set of rules forthe query according to a second set of rules (step 2204). The systemexecutes the query according to the first set of rules to create aprobability of an inference by comparing data in the database (step2206). The system then stores the probability of the first inference andalso stores the inference (step 2208).

The system then performs a recursion process (step 2210). During therecursion process steps 2200 through 2208 are repeated again and again,as each new inference and each new probability becomes a new fact thatcan be used to generate a new probability and a new inference.Additionally, new facts can be received in central database 400 duringthis process, and those new facts also influence the resulting process.Each conclusion or inference generated during the recursion process canbe presented to a user, or only the final conclusion or inference madeafter step 2212 can be presented to a user, or a number of conclusionsmade prior to step 2212 can be presented to a user.

The system then determines whether the recursion process is complete(step 2212). If recursion is not complete, the process between steps2200 and 2210 continues. If recursion is complete, the processterminates.

FIGS. 23A and 23B are a flowchart illustrating execution of a query in adatabase to establish a probability of an inference based on datacontained in the database, in accordance with an illustrativeembodiment. The process shown in FIGS. 23A and 23B can be implementedusing central database 400, data processing network 500, and system M502, all of FIG. 5. In an illustrative embodiment, each of centraldatabase 400, data processing network 500, and system M 502 can beimplemented in a single data processing system or across multiple dataprocessing systems connected by one or more networks. Whetherimplemented in a single data processing system or across multiple dataprocessing systems, taken together all data processing systems,hardware, software, and networks are together referred to as a system.The system implements the process.

The process begins as the system receives an Ith query regarding anI^(th) fact (step 2300). The term “I^(th)” refers to an integer,beginning with one. The integer reflects how many times a recursionprocess, referred to below, has been conducted. Thus, for example, whena query is first submitted that query is the 1^(st) query. The firstrecursion is the 2^(nd) query. The second recursion is the 3^(rd) query,and so forth until recursion I-1 forms the “I^(th)” query. Similarly,but not the same, the I^(th) fact is the fact associated with the I^(th)query. Thus, the 1^(st) fact is associated with the 1^(st) query, the2^(nd) fact is associated with the 2^(nd) query, etc. The I^(th) factcan be the same as previous facts, such as the I^(th)-1 fact, theI^(th)-2 fact, etc. The I^(th) fact can be a compound fact. A compoundfact is a fact that includes multiple sub-facts. The I^(th) fact canstart as a single fact and become a compound fact on subsequentrecursions or iterations. The I^(th) fact is likely to become a compoundfact during recursion, as additional information is added to the centraldatabase during each recursion.

After receiving the I^(th) query, the system establishes the I^(th) factas a frame of reference for the Ith query (step 2302). A frame ofreference is an anchor datum or set of data that is used to limit whichdata are searched in central database 400, that is defines the searchspace. The frame of reference also is used to determine to what rulesthe searched data will be subject. Thus, when the query is executed,sufficient processing power will be available to make inferences.

The system then determines an I^(th) set of rules using a J^(th) set ofrules (step 2304). In other words, a different set of rules is used todetermine the set of rules that are actually applied to the I^(th)query. The term “J^(th)” refers to an integer, starting with one,wherein J=1 is the first iteration of the recursion process and I-1 isthe J^(th) iteration of the recursion process. The J^(th) set of rulesmay or may not change from the previous set, such that J^(th)-1 set ofrules may or may not be the same as the J^(th) set of rules. The term“J^(th)” set of rules refers to the set of rules that establishes thesearch rules, which are the I^(th) set of rules. The J^(th) set of rulesis used to determine the I^(th) set of rules.

The system then determines an I^(th) search space (step 2306). TheI^(th) search space is the search space for the I^(th) iteration. Asearch space is the portion of a database, or a subset of data within adatabase, that is to be searched.

The system then prioritizes the I^(th) set of rules, determined duringstep 2304, in order to determine which rules of the I^(th) set of rulesshould be executed first (step 2308). Additionally, the system canprioritize the remaining rules in the I^(th) set of rules. Again,because computing resources are not infinite, those rules that are mostlikely to produce useful or interesting results are executed first.

After performing steps 2300 through 2306, the system executes the I^(th)query according to the I^(th) set of rules and within the I^(th) searchspace (step 2310). As a result, the system creates an I^(th) probabilityof an Ith inference (step 2312). As described above, the inference is aconclusion based on a comparison of facts within central database 400.The probability of the inference is the likelihood that the inference istrue, or alternatively the probability that the inference is false. TheIth probability and the I^(th) inference need not be the same as theprevious inference and probability in the recursion process, or onevalue could change but not the other. For example, as a result of therecursion process the Ith inference might be the same as the previousiteration in the recursion process, but the I^(th) probability couldincrease or decrease over the previous iteration in the recursionprocess. In contrast, the I^(th) inference can be completely differentthan the inference created in the previous iteration of the recursionprocess, with a probability that is either the same or different thanthe probability generated in the previous iteration of the recursionprocess.

Next, the system stores the I^(th) probability of the I^(th) inferenceas an additional datum in central database 400 (step 2314). Similarly,the system stores the I^(th) inference in central database 400 (step2316), stores a categorization of the probability of the I^(th)inference in central database 400 (step 2318), stores the categorizationof the I^(th) inference in the database (step 2320), stores the rulesthat were triggered in the I^(th) set of rules to generate the I^(th)inference (step 2322), and stores the Ith search space (step 2324).Additional information generated as a result of executing the query canalso be stored at this time. All of the information stored in steps 2314through 2324, and possibly in additional storage steps for additionalinformation, can change how the system performs, how the system behaves,and can change the result during each iteration.

The process then follows two paths simultaneously. First, the systemperforms a recursion process (step 2326) in which steps 2300 through2324 are continually performed, as described above. Second, the systemdetermines whether additional data is received (step 2330).

Additionally, after each recursion, the system determines whether therecursion is complete (step 2328). The process of recursion is completewhen a threshold is met. In one example, a threshold is a probability ofan inference. When the probability of an inference decreases below aparticular number, the recursion is complete and is made to stop. Inanother example, a threshold is a number of recursions. Once the givennumber of recursions is met, the process of recursion stops. Otherthresholds can also be used. If the process of recursion is notcomplete, then recursion continues, beginning again with step 2300.

If the process of recursion is complete, then the process returns tostep 2330. Thus, the system determines whether additional data isreceived at step 2330 during the recursion process in steps 2300 through2324 and after the recursion process is completed at step 2328. Ifadditional data is received, then the system conforms the additionaldata to the database (step 2332), as described with respect to FIG. 18.The system also associates metadata and a key with each additional datum(step 2334). A key uniquely identifies an individual datum. A key can beany unique identifier, such as a series of numbers, alphanumericcharacters, other characters, or other methods of uniquely identifyingobjects.

If the system determines that additional data has not been received atstep 2330, or after associating metadata and a key with each additionaldatum in step 2334, then the system determines whether to modify therecursion process (step 2336). Modification of the recursion process caninclude determining new sets of rules, expanding the search space,performing additional recursions after recursions were completed at step2328, or continuing the recursion process.

In response to a positive determination to modify the recursion processat step 2336, the system again repeats the determination whetheradditional data has been received at step 2330 and also performsadditional recursions from steps 2300 through 2324, as described withrespect to step 2326.

Otherwise, in response to a negative determination to modify therecursion process at step 2336, the system determines whether to executea new query (step 2338). The system can decide to execute a new querybased on an inference derived at step 2312, or can execute a new querybased on a prompt or entry by a user. If the system executes a newquery, then the system can optionally continue recursion at step 2326,begin a new query recursion process at step 2300, or perform bothsimultaneously. Thus, multiple query recursion processes can occur atthe same time. However, if no new query is to be executed at step 2338,then the process terminates.

Thus, the illustrative embodiments provide for creating and using acentralized database for managing information. The centralized databasecan be used to derive probabilities of inferences based on comparison ofdata within the centralized database according to a set of search rules.The centralized database can further be used to prioritize theprobabilities of the inferences and present the probabilities of theinferences to a user according to the prioritization. The search rulesare, themselves, determined by a set of determination rules. Thus, thesystem prevents the entirety of the data in the database from beingcompared in every possible combination in order that limited computingresources can execute desired queries. The system is particularly usefulin the context of criminal investigations or intelligence services wherevast quantities of data are to be sifted. The system is capable oftaking in vast quantities of divergent data and accurately producingprobabilities of inferences based on the divergent data. If possible, asmuch information regarding each datum is stored as metadata associatedwith the corresponding datum. Thus, for example, the source, channel,time of creation, time of modification, time of ownership, ownership,Internet address, whether data is encrypted, encryption methods, andmany other forms of information can be stored as metadata associatedwith each datum. In addition, the metadata associated with each datum isfully searchable and is part of the database search during execution ofa query.

Additionally, the illustrative embodiments provide for a novel class ofprobabilistic inference engines with supporting data structures. Thus,the illustrative embodiments have numerous applications in fields otherthan generating probabilities of inferences regarding criminal orsecurity issues regarding persons, places, events, and other issues. Forexample, the methods and devices described herein can be used to performprivacy and security filtering based on significance levels of data.Thus, data can be made accessible to individuals of different securityaccess clearances based on the probabilities of inferences. Accordingly,a higher or lower threshold of certainty with regard to an inferencecould be required in order for specific data to be made available to theindividuals who are making queries or otherwise manipulating the data.Thus, some measure of privacy can be guaranteed using the methods anddevices described herein. Similarly, the methods and devices describedherein can be used to ensure compliance with medical privacy laws, suchas, for example, HIPPA.

In another illustrative example, the methods and devices describedherein can be used to create probabilities of inferences relating todrugs and drug testing. For example, the illustrative embodiments can beused to generate probabilities of inferences regarding secondary drugeffects over time. Such studies are particularly useful with respect tophase IV drug testing trials involving large numbers of patients. Thus,for example, potentially harmful but difficult to detect side effects ofdrugs could be detected more quickly using the mechanisms of the presentinvention. Similarly, potentially beneficial but difficult to detectside effects of drugs could be detected more quickly using themechanisms of the present invention.

Thus, the illustrative embodiments can be used to determineprobabilities of inferences relating to drugs and further relating totesting of drugs, identifying unknown side effects of drugs, identifyingnew uses for drugs, and/or identifying drugs as being useful fortreating a pre-selected medical condition. In the latter case, apre-selected disease can be identified and the entire field of drugs anddisease related information can be compared in order to identifyprobabilities that one or more drugs would be useful in treating thepre-selected disease.

Additionally, the illustrative embodiments can be used to determineprobabilities of inferences relating to identifying at least oneinteraction of the drug with at least one additional drug. Drugs canhave complex interactions that are not easily identified, and a vastnumber of drugs exist. Thus, the illustrative embodiments areparticularly useful for identifying drug interactions. Similarly, theillustrative embodiments can be used to determine probabilities ofinferences relating to identifying at least one interaction of the drugwith at least one environmental factor. Similarly, the illustrativeembodiments can be used to determine probabilities of inferencesrelating to identifying at least one interaction of the drug with acombination of at least one additional drug, food, and at least oneenvironmental factor.

Moreover, the illustrative embodiments can be used to determineprobabilities of inferences relating to identifying an efficacy of thedrug. As used herein, an efficacy of a drug can relate to how well adrug performs for its intended purpose or for a newly discoveredpurpose.

In another illustrative example, the methods and devices describedherein can be used to discover biological pathways. A biological pathwayis any chain of connected biological functions. Thus, for example, incomplex biological processes, pathways, chains of complex reactions, orchains of events could be discovered. Similarly, in another illustrativeexample, the methods and devices described herein can be used to definethe interaction of known or newly discovered biological pathways and theenvironment.

Thus, for example, a probability of an inference can be related to aninteraction between a biological pathway or a biological system and anenvironmental factor. Examples of biological systems are the nervoussystem, the digestive system, symbiotic systems between cells, systemswithin cells and organelles, and possibly also life cycle systems amonga vast number of organisms. Environmental factors can be any factorexternal to the biological system but that somehow is related to orinteracts with the biological system. Examples of environmental factorsinclude but are not limited to quantifiable factors, such astemperature, pH, and other measurable quantities, and factors for whicha subjective value can be placed, such as security, comfort, and others.

Additionally, the illustrative embodiments can be used to createinferences regarding a relationship between a biological pathway and atleast one of a drug, a food, a substance interacting with the biologicalpathway, a gene, an environmental factor, and combinations thereof. Manydifferent interactions can occur between these factors. In one example,an interaction between statin drugs and grapefruit juice was discoveredafter laborious study. The illustrative embodiments can be used toidentify probabilities of inferences of similar such interactions.

Similarly, affects and proximal affects of biological systems, pathways,environments, and their interactions can be identified. An affect is adirect affect of a biological system, an environment, or an interactionthereof. A proximal affect is some fact or condition that results in thedirect affect or in a chain of additional proximal affects that leads tothe direct affect of the biological system, environment, or aninteraction thereof. Note that biological systems can have an impact onan environment, leading to potentially very complex interactions as thechange in environment in turn leads to additional changes in thebiological systems.

In another illustrative example, the methods and devices describedherein can be used with respect to chaotic events and issues relating toa chaotic event. As used herein, the term “relating to a chaotic event”means any fact, person, or object that can be connected to the chaoticevent, however tangentially.

For example, an illustrative embodiment can be used to determine a causeof a chaotic event or a proximal cause of a chaotic event. A cause is adirect cause of a chaotic event. A proximal cause is some fact orcondition that results in the direct cause or in a chain of additionalproximal causes that leads to the direct cause of the chaotic event. Forexample, probability of a cause of a fire might be determined, alongwith proximal causes of that fire. In a specific example, a faulty wiremight be a cause of the fire and an electrical surge a proximal cause.These facts are all part of a vast plurality of data that might begathered and then processed by the illustrative embodiments.

Another illustrative embodiment can be used to determine an affect of achaotic event. For example, a house is destroyed in a hurricane. Throughthe use of the illustrative embodiments a probability can be determinedthat the house was actually destroyed by a gas explosion. An affect ofthe hurricane could be the felling of a tree, and the felling of thetree broke a gas main, and the broken gas main lead to an explosionafter a spark from an electrical surge. Thus, the illustrativeembodiments can be used to track affects and proximal affects of eventssuch as a hurricane or other chaotic events. Similarly, in theillustrative embodiments, the probability of the first inference can beused to identify one of an affect of the chaotic event, a proximal eventof the chaotic event, and a combination thereof.

Examples of chaotic events include an explosion, a shooting, a gunbattle, deployment of a weapon of mass destruction, a storm, ahurricane, a tornado, an earthquake, a volcanic eruption, an impact ofan extraterrestrial object, a fire, a flood, a tidal wave, a toxicspill, a nuclear meltdown, an infestation of insects, a plague, adisruption of communication systems, a disruption of the Internet, adisruption of economic systems, a riot, an incidence of food poisoning,a mud slide, a rock slide, an avalanche, and combinations thereof.However, may other types of chaotic events exist to which theillustrative embodiments are applicable.

Additionally, the illustrative embodiments are useful for using theprobability inferences to assign administration of aid in response tothe chaotic event. Generally, aid can be any type of aid, includinghumanitarian aid, assignment of resources, assignment of personnel toparticular problems or areas, or any other type of aid. In an example,the illustrative embodiments can be used to assign aid in response tomassive chaotic events, such as Hurricane Katrina. Moreover, theillustrative embodiments can be used to define scored conditions in amass casualty situation. For example, after a major disaster, such asHurricane Katrina, the methods and mechanisms of the present inventioncan be used to track and administer disaster relief as well asprobabilities of inferences of where related disasters (such as levybreaches) might occur and where and how to respond. The presentinvention can also apply to other disaster management processes.

In another illustrative example, similar to the above example relatingto chaotic events, the methods and devices described herein can also beapplied to accident investigation, particularly complex accidentinvestigation. For example, after an airplane crash, potentiallythousands or even millions of parts of an airplane or of passengerremains might be recovered and classified. The present invention can beused to generate, for example, probabilities of inferences of a cause ormultiple causes of the accident based on available data. Once accidentcauses are suspected, the mechanisms of the present invention can beused to create probabilities of inferences that other, similar risksexist in other aircraft. Thus, remedial action can be taken to preventfuture similar accidents.

Non-limiting examples of accidents to which the illustrative embodimentscan be applied an airplane accident, a train accident, a multi-vehicleaccident, a maritime accident, a single vehicle accident, a nuclearmeltdown, a black-out, a building collapse, a failure of a bridge, afailure of a dam, a toxic spill, an explosion, and combinations thereof.The illustrative embodiments can be applied to other accidents.

In addition to investigating the cause of accidents, the illustrativeexamples can be used to assist in administering aid after an accidentand in identifying a cause or proximal cause of an accident. A cause ofan accident is a direct cause of the accident. A proximal cause of anaccident is some fact or condition that results in the direct cause orin a chain of additional proximal causes that leads to the direct causeof the accident. Thus, the illustrative embodiments can be used toidentify one of a cause of the accident, a proximal cause of theaccident, and a combination thereof. Additionally, probability of aninference can be used to assign administration of aid in response to theaccident.

In another illustrative example, the methods and devices describedherein can be used with respect to clinical applications. For example,the illustrative embodiments can be used to discover unobtrusive ordifficult to detect relationships in disease state management. Thus, forexample, the present invention can be used to track complex cases ofcancer or multiply interacting diseases in individual patients.Additionally, patterns of a disease among potentially vast numbers ofpatients can be inferred in order to detect facts relating to one ormore diseases. Furthermore, perhaps after analyzing patterns of adisease in a vast number of patients treated according to differenttreatment protocols, probabilities of success of various treatment planscan be inferred for a particular plan. Thus, another clinicalapplication is determining a treatment plan for a particular patient.

In another clinical application, the methods and devices describedherein can also be used to perform epidemic management and/or diseasecontainment management. Thus, for example, the present invention can beused to monitor possible pandemics, such as the bird flu or possibleterrorist activities, and generate probabilities of inferences of anexplosion of an epidemic and the most likely sites of new infections.

In another clinical application, the methods and devices describedherein can be used to perform quality control in hospitals or othermedical facilities to continuously monitor outcomes. In particular, themethods and devices described herein can be used to monitor undesirableoutcomes, such as hospital borne infections, re-operations, excessmortality, and unexpected transfers to intensive care or emergencydepartments.

In another clinical application, the methods and devices describedherein can be used to perform quality analysis in hospitals or othermedical facilities to determine the root causes of hospital borneinfections. For example, wards, rooms, patient beds, staff members,operating suites, procedures, devices, drugs, or other systematic rootcauses, including multiple causalities can be identified using themethods and devices described herein.

Quality analysis in hospitals, medical facilities, doctors' offices,dentist offices, or other healthcare settings is becoming increasinglyimportant. For example, the U.S. Department of Health and Human Serviceshas specific reporting requirements for hospitals regarding healthcarequality measures. These quality measures are used in governmentalreporting and are posted on governmental websites. Given theavailability of information, healthcare providers are even more keenlyinterested in identifying causes or proximal causes of unsatisfactorypatient outcomes in order to provide better service, reduce patientmortality, increase quality of patient services and recovery, anddecrease liability. A cause is a direct cause of a healthcare outcome. Aproximal cause is some fact or condition that results in the directcause or in a chain of additional proximal causes that leads to thedirect cause of the healthcare outcome.

On the other hand, healthcare providers are also keenly interested inidentifying causes of satisfactory patient outcomes. Thus, healthcareproviders can increase patient survival and improve the quality andlength of patient recovery, while simultaneously reducing the cost ofproviding healthcare services.

State of the art investigation of healthcare setting quality assuranceprovides that an investigator investigate one or more outcomes and workback to a single root cause of those outcomes. This single root causeorientation is often successful in discovering the most significantproblem in a chain of events that led to an outcome. However, analyzingthe relationships among thousands or even millions of facts to producechains of causations has proved to be impossible.

Using the methods and devices described herein, a network ofinterlinking chains of occurrences and the probability of variousoutcomes at each node of the network can be determined. The probabilitydistribution functions of specific outcomes can be expressed as singlepoint estimates, low-medium-high probability estimates, and mathematicalequations representing probability distribution functions of outcomes.Multiple distinct outcomes can be expressed at each node as well astheir associated probability distribution function. The network ofoccurrences can be cross-linked between the causal relationships ofdifferent networks.

In an illustrative example, a budgetary restriction causes a reductionin the training of maintenance persons who clean and sterilize operatingtheaters. Surgical operating room theaters may be totally sterile, mayhave minimal contamination, or may have heavy contamination as a result.The operating room can be contaminated with gram positive organisms,gram negative organisms, or drug resistant organisms. Each of thesecombinations of sterile or contaminated operating rooms have aprobability of occurrence. Thus, for example, the operating room canhave a heavy contamination of gram negative organisms and a lightcontamination of drug resistant organisms.

Continuing the example, a patient scheduled for surgery in thatparticular operating room does not receive antibiotic treatment beforethe scheduled surgical procedure because the treatment is not ordered.The probability of this occurrence could be low, but positive.

During the scheduled procedure, assuming the presence of organisms, thepatient's open incision may not be contaminated or may be contaminatedwith one or more organisms. Thus, for example, the operating room couldbe heavily contaminated with several organisms and the patient couldescape infection.

If the patient had received a prophylactic antibiotic treatment and theorganism is not drug resistant, a probability exists that the patientwill or will not be seriously infected. A probability exists that thepatient was infected, the antibiotic was effective and the patient nevershowed any sign of serious infection. The patient may be infected andtake 4, 8, 12, 16, 20, or 24 hours to beat the infection with associatedprobabilities that the infection was beaten in a given number of hoursor sooner. The probabilities would be expressed as a cumulativeprobability distribution function over the set of timings.

The patient could be infected with a drug resistant organism even if thepatient received the prophylactic antibiotic treatment. The organismcould be defeated by the patient's own immune system within 4, 8, 12,24, 36, 48, or 60 hours. Alternately the doctor could note the infectionand change the antibiotic to one or more of the little used backupantibiotics. The patient then has some probability of responding totreatment or dying. Alternately, a probability exists that the doctorwill not change the antibiotic, resulting in the one of two events: thatthe patient lives or dies.

Staffing levels on the nursing staff can have a significant impact onthe probabilities of noticing and beginning prompt treatment ofinfections. Lack of staff due to inability to hire enough nurses, nursesleaving this hospital to another hospital in the local area whichprovides a more favorable employment environment, or lack of funding forinfection control in-service training can each play a role in changingthe probabilities of bad outcomes. A poor employment environment is notoften cited as a major cause of hospital borne infection, but thatrelationship could be ignored in a typical Bayesian decision tree orfish bone causality chart as used in the known art. The methods anddevices described herein take into account seemingly unrelated factorssuch as poor employment environment in determining chains of causationof an outcome. In the illustrative examples, the causes of bad outcomesare the result of complex webs of interrelationships that can be modeledas a network of interactions.

Thus, the illustrative examples provide for a computer implementedmethod for inferring a probability of a first inference related toidentification of a cause of an outcome in a healthcare setting. Themethod includes receiving a query at a database regarding a fact relatedto the healthcare setting. The fact further relates to a network ofinteractions associated with the outcome. The first inference is absentfrom the database. The database includes a plurality of divergent data.The plurality of divergent data includes a plurality of cohort data.Each datum of the database is conformed to the dimensions of thedatabase. Each datum of the plurality of data has associated metadataand an associated key. The associated metadata comprises data regardingcohorts associated with the corresponding datum, data regardinghierarchies associated with the corresponding datum, data regarding acorresponding source of the datum, and data regarding probabilitiesassociated with integrity, reliability, and importance of eachassociated datum. The method further includes establishing the fact as aframe of reference for the query. The method further includes applying afirst set of rules to the query. The first set of rules are determinedfor the query according to a second set of rules. The first set of rulesdetermine how the plurality of data are to be compared to the fact. Thefirst set of rules determine a search space for the query. The methodfurther includes executing the query to create the probability of thefirst inference. The probability of the first inference is determinedfrom comparing the plurality of data according to the first set ofrules. The method further includes storing the probability of the firstinference.

In an illustrative example the outcome is an undesirable outcome and thecause is a root cause of the undesirable outcome. An undesirable outcomeis any outcome deemed undesirable by a human. A cause can be any cause,including a proximal cause as described above. For example, the proximalcause can be at least one of training of personnel, presence ofpersonnel, unavailability of trained personnel, presence of equipment,absence of equipment, an unsatisfactory working environment, a budgetaryrestriction, a human error, presence of a bacterium, presence of avirus, contamination of an area of the healthcare setting, failure toadminister a drug to at least one patient, failure to administer a drugto the patient for a long enough time, failure to administer acombination of drugs, failure to administer a combination of drugs tothe at least one patient for a long enough time, failure to administer atreatment to the at least one patient, failure to administer a pluralityof treatments to the at least one patient, administration of a procedureon the at least one patient, administration of a combination ofprocedures on the at least one patient, administration of a drug to theat least one patient, administration of a combination of drugs to the atleast one patient, scheduling of administration of procedures,scheduling of administration of drugs, and combinations thereof. Any ofthese proximal causes can be a root cause. Some of these causes, such asbudgetary constraints, can only be proximal causes. Other of thesecauses can be direct causes, such as contamination or a bacterium.

Alternatively, the outcome can be a desirable outcome. A desirableoutcome is any outcome deemed desirable by a human being. In anillustrative example, a desirable outcome is patient recovery. A causeof a desirable outcome could be at least one of training of personnel,presence of personnel, presence of equipment, absence of equipment (froma cost perspective), a working environment having pre-definedcharacteristics, an allocation of funds, a human error that results in apositive outcome, presence of a bacterium that results in a positiveoutcome, presence of a virus that results in a positive outcome,decontamination of an area of the healthcare setting, failure toadminister a drug to at least one patient such that a favorable positiveresults, failure to administer a drug to the patient for a long enoughtime such that a favorable positive results, failure to administer acombination of drugs such that a favorable positive results, failure toadminister a combination of drugs to the at least one patient for a longenough time such that a favorable positive results, failure toadminister a treatment to the at least one patient such that a favorablepositive results, failure to administer a plurality of treatments to theat least one patient such that a favorable positive results,administration of a procedure on the at least one patient,administration of a combination of procedures on the at least onepatient, administration of a drug to the at least one patient,administration of a combination of drugs to the at least one patient,scheduling of administration of procedures, scheduling of administrationof drugs, and combinations thereof.

In an illustrative example, the method described above can furtherinclude expressing a specific outcome as at least one probabilitydistribution function. The at least one probability distributionfunction includes a single point probability estimate, a low-medium-highprobability estimate, and a mathematical equation representing aprobability distribution function of at least one outcome. In this case,the method can further include expressing a number of outcomes as atleast one corresponding probability distribution function expressed ateach node of the network of interactions.

In an illustrative example, the method described above can furtherinclude cross-linking a network of outcomes to the network ofinteractions. Additionally, this method can further include establishinga network of causations among the network of outcomes and the network ofinteractions. Additionally, the method can include establishing aplurality of probabilities of corresponding causations in the network ofcausations at each node of network of interactions.

In another clinical application, the methods and devices describedherein can be used to determine a cause of a disease or a proximal causeof a disease. A cause is a direct cause of a disease. A proximal causeis some fact or condition that results in the direct cause or in a chainof additional proximal causes that leads to the direct cause of thedisease. Thus, for example, a complex interplay of genetics,environmental factors, and lifestyle choices can be examined todetermine a probability that one or more factors or combinations offactors causes a disease or other medical condition.

In another clinical application, the methods and devices describedherein can be used for monitoring public health and public healthinformation using public data sources. For example, the overallpurchasing of over-the-counter drugs can be monitored. People are likelyto self-medicate when they become sick, seeking medical attention onlyif they become very ill or the symptoms of an illness don't abate. Thus,a spike in purchase of over-the-counter drugs in a particulargeographical location can indicate a possible public health problem thatwarrants additional investigation. Possible public health problemsinclude natural epidemics, biological attacks, contaminated watersupplies, contaminated food supplies, and other problems. Additionalinformation, such as specific locations of excessive over-the-counterdrug purchases, time information, and other information can be used tonarrow the cause of a public health problem. Thus, public healthproblems can be quickly identified and isolated using the mechanismsdescribed herein.

A summary of clinical applications, therefore includes determining acause of a disease, determining a proximal cause of a disease,determining a cause of a medical condition, determining a proximal causeof a medical condition, disease state management, medical conditionmanagement, determining a pattern of at least one disease in a pluralityof patients, determining a pattern of at least one medical condition ina plurality of patients, selecting a treatment plan for a particularpatient, determining a genetic factor in relation to a disease,determining a genetic factor in relation to a medical condition,epidemic management, disease containment management, quality control ina medical facility, quality analysis in the medical facility, andmonitoring public health. A medical condition is any condition fromwhich a human or animal can suffer which is undesirable but which is notclassified as a disease.

In another illustrative example, the methods and devices describedherein can be used to perform defect analysis for low frequency, highimpact defects. A low frequency defect is a defect that manifests itselfrelatively infrequently. A high impact defect is a defect that resultsin some kind of relatively catastrophic result or high impact effect ona system. For example, a particular tire manufactured by a particularmanufacturer might be prone to failure when installed on a particulartype of chassis, but only in hot weather conditions. The defect of tireblow-out might occur infrequently because of the required confluence ofevents, but the impact of the defect can be high as a potentiallyserious automobile accident can result. The present invention can beused to generate probabilities of inferences that a low frequency, highimpact defect exists.

In another illustrative example, the methods and devices describedherein can be used for battle management augmentation. Battles, fromsmall firefights to large scale engagements, are subject to rapidlychanging conditions. Commanders must make decisions very quickly basedon available information. Available information can be a great deal ofinformation, given modern information gathering techniques used inmodern battle management, though the information might be incomplete orvague. The illustrative embodiments can be used to manage thepotentially vast amount of information available to aid commanders inmaking decisions during battle.

In another illustrative example, the methods and devices describedherein can be used to perform geo-economic impact analysis. Ingeo-economic impact analysis, a comparison is made among changes inenvironment to changes in quality of life and local economics.Geo-economic impact analysis is especially useful in urban environments.For example, how does the quality of life in an urban environment changewhen several windows are broken, but unrepaired. In another example,changes in quality of life can be analyzed based on which laws governingminor infractions are enforced.

In another illustrative example, the methods and devices describedherein can be used to monitor employee retention for hard-to-fill jobssuch as nursing jobs, laboratory technician jobs, radiologist jobs,legal jobs, executive jobs, or any other job in which a high degree ofexpertise is required. For example, compensation packages, workingconditions, working environment, perquisites, work hours, stress,skills, work habits, personal habits, and other factors can be comparedin order to determine which overall combinations of work environmentsand compensation packages will most likely result in maximum employeeretention.

In another illustrative example, the methods and devices describedherein can be used to monitor gangs and gang related activities. Forexample, the detailed social structures of gangs can be tracked,including hierarchies, members, propensity to various illegalactivities, and the recruitment techniques for attracting new members.Thus, the methods and devices described herein can be used to both trackand deter criminal gangs, but also to limit new recruits for criminalgangs.

In another illustrative example, the methods and devices describedherein can be used by human resource departments in medium to largeorganizations to determine individual level skills by examination ofparticipation in sales opportunities. This type of data collection canbe performed by a variety of known software packages, such as Siebel, acustomer relationship management software package available from OracleCorporation. The methods and devices described herein can useinformation acquired by Siebel, manual data input, and other sources todetermine the relative success of individuals on classes of salesopportunities. This analysis would also show gaps in skills that shouldbe addressed by training existing employees or by hiring additionalemployees with the desired skills.

In another illustrative example, the methods and devices describedherein can be used to monitor tax advisors and tax payers for patternsof tax fraud. For example, the relationships between individuals who donot pay taxes, tax preparers and other individuals, locations, and timescan be used to generate inferences regarding specific tax preparers andtax avoidance transactions. This information can be used to determinecohorts of tax payers relying on similar tax avoidance schemes. Thus,the methods and devices described herein can be used to identify taxfraud, aid prosecution of those who commit tax fraud, and potentiallyincrease tax revenue.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer implemented method for inferring a probability of a firstinference related to identification of a cause of an outcome in ahealthcare setting, the computer implemented method comprising:receiving a query at a database regarding a fact related to thehealthcare setting, wherein the fact further relates to a network ofinteractions associated with the outcome, wherein the first inference isabsent from the database, wherein the database comprises a plurality ofdivergent data, wherein the plurality of divergent data includes aplurality of cohort data, wherein each datum of the database isconformed to the dimensions of the database, wherein each datum of theplurality of data has associated metadata and an associated key, whereinthe associated metadata comprises data specifying cohorts associatedwith the corresponding datum, data specifying hierarchies associatedwith the corresponding datum, data specifying a corresponding source ofthe datum, and data specifying probabilities associated with integrity,reliability, and importance of each associated datum; establishing thefact as a frame of reference for the query; applying a first set ofrules to the query, wherein the first set of rules are determined forthe query according to a second set of rules, wherein the first set ofrules determine how the plurality of data are to be compared to thefact, and wherein the first set of rules determine a search space forthe query; executing the query to create the probability of the firstinference, wherein the probability of the first inference is determinedfrom comparing the plurality of data according to the first set ofrules; and storing the probability of the first inference.
 2. Thecomputer implemented method of claim 1 wherein the outcome is anundesirable outcome and wherein the cause is a root cause of theundesirable outcome.
 3. The computer implemented method of claim 1wherein the outcome is an undesirable outcome, wherein the cause is aproximal cause, and wherein the cause comprises at least one of trainingof personnel, presence of personnel, unavailability of trainedpersonnel, presence of equipment, absence of equipment, anunsatisfactory working environment, a budgetary restriction, a humanerror, presence of a bacterium, presence of a virus, contamination of anarea of the healthcare setting, failure to administer a drug to at leastone patient, failure to administer a drug to the patient for a longenough time, failure to administer a combination of drugs, failure toadminister a combination of drugs to the at least one patient for a longenough time, failure to administer a treatment to the at least onepatient, failure to administer a plurality of treatments to the at leastone patient, administration of a procedure on the at least one patient,administration of a combination of procedures on the at least onepatient, administration of a drug to the at least one patient,administration of a combination of drugs to the at least one patient,scheduling of administration of procedures, scheduling of administrationof drugs, and combinations thereof.
 4. The computer implemented methodof claim 3 wherein one of the at least one cause comprises a root causeof the undesirable outcome.
 5. The computer implemented method of claim1 wherein the outcome comprises a desirable outcome.
 6. The computerimplemented method of claim 5 wherein the cause comprises at least oneof training of personnel, presence of personnel, presence of equipment,absence of equipment, a working environment having pre-definedcharacteristics, an allocation of funds, a human error, presence of abacterium, presence of a virus, decontamination of an area of thehealthcare setting, failure to administer a drug to at least onepatient, failure to administer a drug to the patient for a long enoughtime, failure to administer a combination of drugs, failure toadminister a combination of drugs to the at least one patient for a longenough time, failure to administer a treatment to the at least onepatient, failure to administer a plurality of treatments to the at leastone patient, administration of a procedure on the at least one patient,administration of a combination of procedures on the at least onepatient, administration of a drug to the at least one patient,administration of a combination of drugs to the at least one patient,scheduling of administration of procedures, scheduling of administrationof drugs, and combinations thereof.
 7. The computer implemented methodof claim 6 further comprising: executing a second query to create asecond probability of a second inference, wherein the second probabilityof the second inference is determined from comparing the plurality ofdata according to the set of rules.
 8. The computer implemented methodof claim 1 further comprising: responsive to receiving an additionaldatum in the database, establishing a second query; applying a third setof rules to the query, wherein the third set of rules are determined forthe second query according to a fourth set of rules, and wherein thethird set of rules determine how the plurality of data are to becompared to the fact; and executing the second query to create a secondprobability of a second inference, wherein the second probability of thesecond inference is determined from comparing the plurality of dataaccording to the third set of rules.
 9. The computer implemented methodof claim 1 further comprising: expressing a specific outcome as at leastone probability distribution function.
 10. The computer implementedmethod of claim 9 wherein the at least one probability distributionfunction comprises at least one of a single-point probability estimate,a low-medium-high probability estimate, and a mathematical equationrepresenting a probability distribution function of at least oneoutcome.
 11. The computer implemented method of claim 10 furthercomprising expressing a plurality of outcomes as at least onecorresponding probability distribution function expressed at each nodeof the network of interactions.
 12. The computer implemented method ofclaim 1 further comprising: cross-linking a network of outcomes to thenetwork of interactions.
 13. The computer implemented method of claim 12further comprising: establishing a network of causations among thenetwork of outcomes and the network of interactions.
 14. The computerimplemented method of claim 13 further comprising: establishing aplurality of probabilities of corresponding causations in the network ofcausations at each node of the network of interactions.
 15. The computerimplemented method of claim 1 wherein the set of rules includes rulesfor adjusting the probability of the inference based on background data.16. A database stored in a recordable-type medium, wherein the databaseis related to identification of a cause of an outcome in a healthcaresetting, the database comprising: a plurality of divergent data storedin a data structure on the recordable-type medium, wherein the pluralityof divergent data includes a plurality of cohort data, wherein eachdatum of the database is conformed to the dimensions of the database,wherein each datum of the plurality of data has associated metadata andan associated key, wherein the associated metadata comprises dataspecifying cohorts associated with the corresponding datum, dataspecifying hierarchies associated with the corresponding datum, dataspecifying representing a corresponding source of the datum, and dataspecifying probabilities associated with integrity, reliability, andimportance of each associated datum; computer usable program code forestablishing a fact related to the healthcare setting, wherein the factfurther relates to a network of interactions associated with theoutcome, received in a query, as a frame of reference for the query;computer usable program code for applying a first set of rules to thequery, wherein the first set of rules are determined for the queryaccording to a second set of rules, wherein the first set of rulesdetermine how the plurality of data are to be compared to the fact, andwherein the first set of rules determine a search space for the query;computer usable program code for executing the query to create aprobability of a first inference, wherein the probability of the firstinference is determined from comparing the plurality of data accordingto the first set of rules; and computer usable program code for storingthe probability of the first inference.
 17. The database of claim 16wherein the outcome is an undesirable outcome and wherein the cause is aroot cause of the undesirable outcome.
 18. The database of claim 16wherein the outcome is an undesirable outcome and wherein the causecomprises at least one of training of personnel, presence of personnel,unavailability of trained personnel, presence of equipment, absence ofequipment, an unsatisfactory working environment, a budgetaryrestriction, a human error, presence of a bacterium, presence of avirus, contamination of an area of the healthcare setting, failure toadminister a drug to at least one patient, failure to administer a drugto the patient for a long enough time, failure to administer acombination of drugs, failure to administer a combination of drugs tothe at least one patient for a long enough time, failure to administer atreatment to the at least one patient, failure to administer a pluralityof treatments to the at least one patient, administration of a procedureon the at least one patient, administration of a combination ofprocedures on the at least one patient, administration of a drug to theat least one patient, administration of a combination of drugs to the atleast one patient, scheduling of administration of procedures,scheduling of administration of drugs, and combinations thereof.
 19. Thedatabase of claim 18 further comprising: computer usable program codefor cross-linking a network of outcomes to the network of interactions.20. The database of claim 19 further comprising: computer usable programcode for establishing a network of causations among the network ofoutcomes and the network of interactions; and computer usable programcode for establishing a plurality of probabilities of correspondingcausations in the network of causations at each node of network ofinteractions.